Big Data Analytics in Structural Diagnosis and Prognosis

Abstract

This paper investigates the use of big data analytics in uncertainty quantification, and applies the proposed framework to structural diagnosis and prognosis. As smart sensor technology is making progress and low cost online monitoring is becoming increasingly possible, large quantities of data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, velocity and variety of sensor data. Next, both forward and inverse problems in uncertainty quantification are investigated with this efficient computational approach. We use Bayesian methods for the inverse problem of diagnosis, and parallelize the numerical integration techniques such as Markov chain Monte Carlo simulation and particle filter. To predict damage growth and the structures remaining useful life (forward problem), Monte Carlo simulation is used to propagate the uncertainties (both aleatory and epistemic) to the future state. MapReduce is again applied to drive the parallelization of multiple FEA runs, thus greatly saving the computational cost. The proposed techniques are illustrated for the diagnosis and prognosis of alkali-silica reaction in a concrete structure